Wald_Friedman_jl.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## A Problem that stumped Milton Friedman\n",
    "\n",
    "(and that Abraham Wald solved by inventing sequential analysis)\n",
    "\n",
    "#### By [Chase Coleman](https://github.com/cc7768) and [Thomas J. Sargent](http://www.tomsargent.com/), edited by [Alberto Polo](https://github.com/albep) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by importing some packages called by the code that we will be using in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using Distributions\n",
    "using QuantEcon.compute_fixed_point, QuantEcon.DiscreteRV, QuantEcon.draw, QuantEcon.LinInterp\n",
    "using Plots\n",
    "using LaTeXStrings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential analysis\n",
    "\n",
    "Key ideas in  play are:\n",
    "\n",
    " * Bayes' Law\n",
    " \n",
    " * Dynamic programming\n",
    "\n",
    " * type I and type II statistical errors\n",
    "    \n",
    "    * a type I error occurs when you reject a  null hypothesis that is true\n",
    "    \n",
    "    * a type II error is when you accept a null hypothesis that is false \n",
    " \n",
    "\n",
    " * Abraham Wald's **sequential probability ratio test**\n",
    " \n",
    " * The **power** of a statistical test\n",
    " \n",
    " * The **critical region** of a statistical test\n",
    " \n",
    " * A **uniformly most powerful test**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On pages 137-139 of his book **Two Lucky People** with Rose Friedman, Milton Friedman described a problem presented to him and Allen Wallis during World War II when they worked at the U.S. government's Statistical Research Group at Columbia University.  \n",
    "\n",
    "Let's listen to Milton Friedman tell what happened.\n",
    "\n",
    "   \"In order to understand the story, it is necessary to have an idea of a simple statistical problem, and of the\n",
    "   standard procedure for dealing with it.  The actual problem out of which sequential analysis grew will serve.\n",
    "   The Navy has two alternative designs (say A and B) for a projectile.  It wants to determine which is superior. \n",
    "   To do so it undertakes a series of paired firings. On each round it assigns the value 1 or 0 to A accordingly as\n",
    "   its performance is superior or inferio to that of B and conversely 0 or 1 to B.  The Navy asks the statistician \n",
    "   how to conduct the test and how to analyze the results. \n",
    "   \n",
    "   \"The standard statistical answer was to specify a number of firings (say 1,000) and a pair of percentages\n",
    "   (e.g., 53% and 47%) and tell the client that if A receives a 1 in more than 53% of the firings, it can be regarded\n",
    "   as superior; if it receives a 1 in fewer than 47%, B can be regarded as superior; if the percentage is between\n",
    "   47% and 53%, neither can be so regarded.\n",
    "   \n",
    "   \"When Allen Wallis was discussing such a problem with (Navy) Captain Garret L. Schyler, the captain objected that     such a test, to quote from Allen's account, may prove wasteful.  If a wise and seasoned ordnance officer like   Schyler were on the premises, he would see after the first few thousand or even few hundred [rounds] that the   experiment need not be completed either because he new method is obviously inferior or because it is obviously   superior beyond what was hoped for $\\ldots$ \"\n",
    "  \n",
    "  Friedman and Wallis struggled with the problem but after realizing that they were not able to solve it themselves told Abraham Wald it.  That started  Wald on the path that led  *Sequential Analysis*. We'll formulate the problem using dynamic programming.\n",
    "\n",
    "This started Wald on the path that led him to _Sequential Analysis_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic programming formulation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following presentation of the problem closely follows Dmitri Berskekas's treatment in **Dynamic Programming and Stochastic Control**.  \n",
    "\n",
    "An i.i.d.  random variable $z$ can take on values \n",
    "\n",
    "  * $z \\in [ v_1,  v_2, \\ldots,  v_n]$ when $z$ is a discrete-valued random variable\n",
    "  \n",
    "  * $ z \\in V$ when $z$ is a continuous random variable.  \n",
    "\n",
    "A decision maker wants to  know which of two probability distributions governs  $z$. To formalize this idea,\n",
    "let $x \\in [x_0, x_1]$ be a hidden state that indexes the two distributions:\n",
    "\n",
    "$$ P(v_k \\mid x) = \\begin{cases} f_0(v_k) & \\mbox{if } x = x_0, \\\\\n",
    "                              f_1(v_k) & \\mbox{if } x = x_1. \\end{cases} $$\n",
    "                              \n",
    "when $z$ is a discrete random variable and a density\n",
    "\n",
    "\n",
    "$$ P(v \\mid x) = \\begin{cases} f_0(v) & \\mbox{if } x = x_0, \\\\\n",
    "                              f_1(v) & \\mbox{if } x = x_1. \\end{cases} $$\n",
    "                              \n",
    "when $v$ is continuously distributed.                              \n",
    "\n",
    "                              \n",
    "\n",
    "Before observing any outcomes, a decision maker believes that the probability that $x = x_0$ is $p_{-1}\\in (0,1)$: \n",
    "\n",
    "$$ p_{-1} = \\textrm{Prob}(x=x_0 \\mid \\textrm{ no observations}) $$\n",
    "\n",
    "After observing $k+1$ observations $z_k, z_{k-1}, \\ldots, z_0$ he believes that the probability that the distribution is $f_0$ is\n",
    "\n",
    "$$ p_k = {\\rm Prob} ( x = x_0 \\mid z_k, z_{k-1}, \\ldots, z_0) $$\n",
    "\n",
    "We can compute this $p_k$ recursively by applying Bayes' law:\n",
    "\n",
    "$$ p_0 = \\frac{ p_{-1} f_0(z_0)}{ p_{-1} f_0(z_0) + (1-p_{-1}) f_1(z_0) } $$\n",
    "\n",
    "and then\n",
    "\n",
    "$$ p_{k+1} = \\frac{ p_k f_0(z_{k+1})}{ p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1}) }. $$\n",
    "\n",
    "\n",
    "After observing $z_k, z_{k-1}, \\ldots, z_0$, the decision maker believes that $z_{k+1}$ \n",
    "has probability distribution\n",
    "\n",
    "$$ p(z_{k+1}) = p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1}). $$\n",
    "\n",
    "This is evidently a mixture of distributions $f_0$ and $f_1$, with the weight on $f_0$ being the posterior probability $f_0$ that the distribution is $f_0$. \n",
    "\n",
    "**Remark:** *Because the decision maker believes that $z_{k+1}$ is drawn from a mixture of two i.i.d. distributions, he does *not* believe that the sequence  $[z_{k+1}, z_{k+2}, \\ldots] $ is i.i.d.  Instead, he believes that it is *exchangeable*.  See David Kreps\n",
    "*Notes on the Theory of Choice*, chapter 11, for a discussion.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some examples of two distributions. Here we'll display two beta distributions.  First, we'll show the two distributions, then we'll show mixtures of these same two distributions with various mixing probabilities $p_k$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create two distributions over 50 values for k\n",
    "# We are using a discretized beta distribution\n",
    "\n",
    "p_m1 = linspace(0, 1, 50)\n",
    "f0 = clamp(pdf(Beta(1, 1), p_m1), 1e-8, Inf)\n",
    "f0 = f0 / sum(f0)\n",
    "f1 = clamp(pdf(Beta(9, 9), p_m1), 1e-8, Inf)\n",
    "f1 = f1 / sum(f1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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\" />"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = plot([f0 f1], \n",
    "    xlabel=L\"$k$ Values\",\n",
    "    ylabel=L\"Probability of $z_k$\",\n",
    "    labels=reshape([L\"$f_0$\"; L\"$f_1$\"], 1, 2),\n",
    "    linewidth=2,\n",
    "    ylims=[0.;0.07],\n",
    "    title=\"Original Distributions\")\n",
    "\n",
    "mix = Array(Float64, 50, 3)\n",
    "p_k = [0.25; 0.5; 0.75]\n",
    "labels = Array(String, 3)\n",
    "for i in 1:3\n",
    "    mix[:, i] = p_k[i] * f0 + (1 - p_k[i]) * f1\n",
    "    labels[i] = string(L\"$p_k$ = \", p_k[i])\n",
    "end\n",
    "    \n",
    "b = plot(mix, \n",
    "    xlabel=L\"$k$ Values\",\n",
    "    ylabel=L\"Probability of $z_k$\",\n",
    "    labels=reshape(labels, 1, 3),\n",
    "    linewidth=2,\n",
    "    ylims=[0.;0.06],\n",
    "    title=\"Mixture of Original Distributions\")\n",
    "\n",
    "plot(a, b, layout=(2, 1), size=(600, 800))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Losses and costs\n",
    "\n",
    "\n",
    "After observing $z_k, z_{k-1}, \\ldots, z_0$, the decision maker chooses among three distinct actions:\n",
    "\n",
    "* He decides that $x = x_1$ and draws no more $z$'s\n",
    "\n",
    "* He decides that $x = x_0$ and draws no more $z$'s\n",
    "\n",
    "* He postpones deciding now and instead chooses to draw a $z_{k+1}$\n",
    "\n",
    "Associated with these three actions, the decision maker suffers three kinds of losses:\n",
    "\n",
    " \n",
    "* A loss $L_0$ if he decides $x = x_0$ when actually $x=x_1$\n",
    "\n",
    "* A loss $L_1$ if he decides $x = x_1$ when actually $x=x_0$\n",
    "\n",
    "* A cost $c$ if he postpones deciding and chooses instead to draw another  $z$ \n",
    "\n",
    "For example, suppose that we regard $x=x_0$ as a null hypothesis. Then  \n",
    "\n",
    "* We can think of $L_1$ as the loss associated with a type I error\n",
    "\n",
    "* We can think of $L_0$ as the loss associated with a type II error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Bellman equation\n",
    "\n",
    "Let $J_k(p_k)$ be the  total loss for a decision maker who with posterior probability $p_k$ who chooses optimally.\n",
    "\n",
    "The loss functions $\\{J_k(p_k)\\}_k$ satisfy the Bellman equations\n",
    "\n",
    "$$ J_k(p_k) = \\min \\left[ (1-p_k) L_0, p_k L_1, c + E_{z_{k+1}} \\left\\{ J_{k+1} (p_{k+1} \\right\\} \\right] $$\n",
    "\n",
    "where $E_{z_{k+1}}$ denotes a mathematical expectation over the distribution of $z_{k+1}$ and  the minimization is over the three actions, accept $x_0$, accept $x_1$, and postpone deciding and draw \n",
    "a $z_{k+1}$.  \n",
    "\n",
    "Let \n",
    "\n",
    "$$ A_k(p_k) = E_{z_{k+1}} \\left\\{ J_{k+1} \\left[\\frac{ p_k f_0(z_{k+1})}{ p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1})  } \\right] \\right\\} $$\n",
    "\n",
    "Then we can write out Bellman equation as\n",
    "\n",
    "$$ J_k(p_k) = \\min \\left[ (1-p_k) L_0, p_k L_1, c + A_k(p_k) \\right] $$\n",
    "\n",
    "where $p_k \\in [0,1]$.  \n",
    "\n",
    "Evidently,the optimal decision rule is characterized by two numbers $\\alpha_k, \\beta_k \\in (0,1) \\times (0,1)$\n",
    "that satisfy\n",
    "\n",
    "$$ (1- p_k) L_0 < \\min p_k L_1, c + A_k(p_k)  \\textrm { if } p_k \\geq \\alpha_k  $$\n",
    "\n",
    "and \n",
    "\n",
    "$$ p_k L_1 < \\min (1-p_k) L_0,  c + A_k(p_k) \\textrm { if } p_k \\leq \\beta_k $$\n",
    "\n",
    "The optimal decision rule is then\n",
    "\n",
    "$$ \\textrm { accept } x=x_0 \\textrm{ if } p_k \\geq \\alpha_k \\\\\n",
    "   \\textrm { accept } x=x_1 \\textrm{ if } p_k \\leq \\beta_k \\\\\n",
    "   \\textrm { draw another }  z \\textrm{ if }  \\beta_k \\leq p_k \\leq \\alpha_k $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infinite horizon version\n",
    "\n",
    "An infinite horizon version of this problem is associated with the limiting Bellman equation \n",
    "\n",
    "$$ J(p_k) = \\min \\left[ (1-p_k) L_0, p_k L_1, c + A(p_k) \\right] \\quad (*) $$\n",
    "\n",
    "where\n",
    "\n",
    "$$ A(p_k) = E_{z_{k+1}} \\left\\{ J \\left[\\frac{ p_k f_0(z_{k+1})}{ p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1})  } \\right] \\right\\} $$\n",
    "\n",
    "and again the minimization is over the three actions, accept $x_1$, accept $x_0$, and postpone deciding and draw \n",
    "a $z_{k+1}$.\n",
    "\n",
    "Here\n",
    "\n",
    "  * $ (1-p_k) L_0$ is the expected loss associated with accepting $x_1$ (i.e., the cost of making a type I error)\n",
    "  \n",
    "  *  $p_k L_1$ is the expected loss associated with accepting $x_0$ (i.e., the cost of making a type II error)\n",
    "  \n",
    "  * $ c + A(p_k)$ is the expected cost associated with drawing one more  $z$\n",
    "\n",
    "\n",
    "Now the optimal decision rule is characterized by two probabilities $0 < \\beta < \\alpha < 1$ and \n",
    "\n",
    "\n",
    "$$ \\textrm { accept } x=x_0 \\textrm{ if } p_k \\geq \\alpha \\\\\n",
    "   \\textrm { accept } x=x_1 \\textrm{ if } p_k \\leq \\beta \\\\\n",
    "   \\textrm { draw another }  z \\textrm{ if } \\beta \\leq p_k \\leq \\alpha $$\n",
    "\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One sensible approach is to write the three components of the value function that appears on the rights side of the Bellman equation as separate functions.  Later, doing this will help us obey the don't repeat yourself (DRY) rule of coding.  Here goes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "For a given probability return expected loss of choosing model 0\n",
    "\"\"\"\n",
    "function expect_loss_choose_0(p, L0)\n",
    "    return (1-p)*L0\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "For a given probability return expected loss of choosing model 1\n",
    "\"\"\"\n",
    "function expect_loss_choose_1(p, L1)\n",
    "    return p*L1\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "We will need to be able to evaluate the expectation of our Bellman\n",
    "equation J. In order to do this, we need the current probability\n",
    "that model 0 is correct (p), the distributions (f0, f1), and a\n",
    "function that can evaluate the Bellman equation\n",
    "\"\"\"\n",
    "function EJ(p, f0, f1, J)\n",
    "    # Get the current distribution we believe (p*f0 + (1-p)*f1)\n",
    "    curr_dist = p*f0 + (1-p)*f1\n",
    "    \n",
    "    # Get tomorrow's expected distribution through Bayes law\n",
    "    tp1_dist = clamp((p*f0) ./ (p*f0 + (1-p)*f1), 0, 1)\n",
    "    \n",
    "    # Evaluate the expectation\n",
    "    EJ = dot(curr_dist, J.(tp1_dist))\n",
    "    \n",
    "    return EJ\n",
    "end\n",
    "    \n",
    "expect_loss_cont(p, c, f0, f1, J) = c + EJ(p, f0, f1, J);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To approximate the solution of the Bellman equation (\\*) above, we can deploy a method known as value function iteration (iterating on the Bellman equation) on a grid of points. Because we are iterating on a grid, the current probability, $p_k$, is restricted to a set number of points. However, in order to evaluate the expectation of the Bellman equation for tomorrow, $A(p_{k})$, we must be able to evaluate at various $p_{k+1}$ which may or may not correspond with points on our grid. The way that we resolve this issue is by using *linear interpolation*. This means to evaluate $J(p)$ where $p$ is not a grid point, we must use two points: first, we use the largest of all the grid points smaller than $p$, and call it $p_i$, and, second, we use the grid point immediately after $p$, named $p_{i+1}$, to approximate the function value in the following manner:\n",
    "\n",
    "$$ J(p) = J(p_i) + (p - p_i) \\frac{J(p_{i+1}) - J(p_i)}{p_{i+1} - p_{i}}$$\n",
    "\n",
    "In one dimension, you can think of this as simply drawing a line between each pair of points on the grid.\n",
    "\n",
    "For more information on both linear interpolation and value function iteration methods, see the Quant-Econ [lecture](http://quant-econ.net/py/ifp.html) on the income fluctuation problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute iterate 5 with error 0.08552607733051265\n",
      "Compute iterate 10 with error 0.00038782894418165625\n",
      "Compute iterate 15 with error 1.6097835344730527e-6\n",
      "Converged in 16 steps\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Evaluates the value function for a given continuation value\n",
    "function; that is, evaluates\n",
    "\n",
    "    J(p) = min(pL0, (1-p)L1, c + E[J(p')])\n",
    "\n",
    "Uses linear interpolation between points\n",
    "\"\"\"\n",
    "function bellman_operator(pgrid, c, f0, f1, L0, L1, J)\n",
    "    m = length(pgrid)\n",
    "    @assert m == length(J)\n",
    "    \n",
    "    J_out = zeros(m)\n",
    "    J_interp = LinInterp(pgrid, J)\n",
    "\n",
    "    for (p_ind, p) in enumerate(pgrid)\n",
    "        # Payoff of choosing model 0\n",
    "        p_c_0 = expect_loss_choose_0(p, L0)\n",
    "        p_c_1 = expect_loss_choose_1(p, L1)\n",
    "        p_con = expect_loss_cont(p, c, f0, f1, J_interp)\n",
    "        \n",
    "        J_out[p_ind] = min(p_c_0, p_c_1, p_con)\n",
    "    end\n",
    "        \n",
    "    return J_out\n",
    "end\n",
    "\n",
    "# To solve\n",
    "pg = linspace(0, 1, 251)\n",
    "J1 = compute_fixed_point(x -> bellman_operator(pg, 0.5, f0, f1, 5.0, 5.0, x),\n",
    "    zeros(length(pg)), err_tol=1e-6, print_skip=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for some gentle criticisms of the preceding code. Although it works fine, by writing the code in terms of  functions, we have to  pass around some things that are constant throughout the problem,  i.e., $c$, $f_0$, $f_1$, $L_0$, and $L_1$. \n",
    "\n",
    "Now that we have a working script, let's turn it into types and type-specific methods for each function.  This will allow us to simplify the function calls and make the code more reusable.\n",
    "\n",
    "So to illustrate a good alternative approach, we write a type that stores all of our parameters for us internally, as well as the model solution, and is used by many of the same functions that we used above.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stopping_dist"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "This type is used to store the solution to the problem presented \n",
    "in the \"Wald Friedman\" notebook presented on the QuantEcon website.\n",
    "\n",
    "Solution\n",
    "----------\n",
    "J : vector(Float64)\n",
    "    Discretized value function that solves the Bellman equation\n",
    "lb : scalar(Real)\n",
    "    Lower cutoff for continuation decision\n",
    "ub : scalar(Real)\n",
    "    Upper cutoff for continuation decision\n",
    "\"\"\"\n",
    "type WFSolution\n",
    "    J::Vector{Float64}\n",
    "    lb::Real\n",
    "    ub::Real\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This type is used to solve the problem presented in the \"Wald Friedman\"\n",
    "notebook presented on the QuantEcon website.\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "c : scalar(Real)\n",
    "    Cost of postponing decision\n",
    "L0 : scalar(Real)\n",
    "    Cost of choosing model 0 when the truth is model 1\n",
    "L1 : scalar(Real)\n",
    "    Cost of choosing model 1 when the truth is model 0\n",
    "f0 : vector(Float64)\n",
    "    A finite state probability distribution\n",
    "f1 : vector(Float64)\n",
    "    A finite state probability distribution\n",
    "m : scalar(Int64)\n",
    "    Number of points to use in function approximation\n",
    "\"\"\"\n",
    "immutable WaldFriedman\n",
    "    c::Real\n",
    "    L0::Real\n",
    "    L1::Real\n",
    "    f0::Vector{Float64}\n",
    "    f1::Vector{Float64}\n",
    "    m::Int64\n",
    "    pgrid::LinSpace{Float64}\n",
    "    sol::WFSolution\n",
    "end\n",
    "\n",
    "function WaldFriedman(c, L0, L1, f0, f1; m=25)\n",
    "    pgrid = linspace(0.0, 1.0, m)\n",
    "\n",
    "    # Renormalize distributions so nothing is \"too\" small\n",
    "    f0 = clamp(f0, 1e-8, 1-1e-8)\n",
    "    f1 = clamp(f1, 1e-8, 1-1e-8)\n",
    "    f0 = f0 / sum(f0)\n",
    "    f1 = f1 / sum(f1)\n",
    "    J = zeros(m)\n",
    "    lb = 0.\n",
    "    ub = 0.\n",
    "    \n",
    "    WaldFriedman(c, L0, L1, f0, f1, m, pgrid, WFSolution(J, lb, ub))\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This function takes a value for the probability with which\n",
    "the correct model is model 0 and returns the mixed\n",
    "distribution that corresponds with that belief.\n",
    "\"\"\"\n",
    "function current_distribution(wf::WaldFriedman, p)\n",
    "    return p*wf.f0 + (1-p)*wf.f1\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This function takes a value for p, and a realization of the\n",
    "random variable and calculates the value for p tomorrow.\n",
    "\"\"\"\n",
    "function bayes_update_k(wf::WaldFriedman, p, k)\n",
    "    f0_k = wf.f0[k]\n",
    "    f1_k = wf.f1[k]\n",
    "\n",
    "    p_tp1 = p*f0_k / (p*f0_k + (1-p)*f1_k)\n",
    "\n",
    "    return clamp(p_tp1, 0, 1)\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This is similar to `bayes_update_k` except it returns a\n",
    "new value for p for each realization of the random variable\n",
    "\"\"\"\n",
    "function bayes_update_all(wf::WaldFriedman, p)\n",
    "    return clamp(p*wf.f0 ./ (p*wf.f0 + (1-p)*wf.f1), 0, 1)\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "For a given probability specify the cost of accepting model 0\n",
    "\"\"\"\n",
    "function payoff_choose_f0(wf::WaldFriedman, p)\n",
    "    return (1-p)*wf.L0\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "For a given probability specify the cost of accepting model 1\n",
    "\"\"\"\n",
    "function payoff_choose_f1(wf::WaldFriedman, p)\n",
    "    return p*wf.L1\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This function evaluates the expectation of the value function\n",
    "at period t+1. It does so by taking the current probability\n",
    "distribution over outcomes:\n",
    "\n",
    "    p(z_{k+1}) = p_k f_0(z_{k+1}) + (1-p_k) f_1(z_{k+1})\n",
    "\n",
    "and evaluating the value function at the possible states\n",
    "tomorrow J(p_{t+1}) where\n",
    "\n",
    "    p_{t+1} = p f0 / ( p f0 + (1-p) f1)\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "p : scalar\n",
    "    The current believed probability that model 0 is the true\n",
    "    model.\n",
    "J : function (interpolant)\n",
    "    The current value function for a decision to continue\n",
    "\n",
    "Returns\n",
    "-------\n",
    "EJ : scalar\n",
    "    The expected value of the value function tomorrow\n",
    "\"\"\"\n",
    "function EJ(wf::WaldFriedman, p, J)\n",
    "    # Pull out information\n",
    "    f0, f1 = wf.f0, wf.f1\n",
    "\n",
    "    # Get the current believed distribution and tomorrows possible dists\n",
    "    # Need to clip to make sure things don't blow up (go to infinity)\n",
    "    curr_dist = current_distribution(wf, p)\n",
    "    tp1_dist = bayes_update_all(wf, p)\n",
    "   \n",
    "    # Evaluate the expectation\n",
    "    EJ = dot(curr_dist, J.(tp1_dist))\n",
    "\n",
    "    return EJ\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "For a given probability distribution and value function give\n",
    "cost of continuing the search for correct model\n",
    "\"\"\"\n",
    "function payoff_continue(wf::WaldFriedman, p, J)\n",
    "    return wf.c + EJ(wf, p, J)\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "Evaluates the value function for a given continuation value\n",
    "function; that is, evaluates\n",
    "\n",
    "    J(p) = min( (1-p)L0, pL1, c + E[J(p')])\n",
    "\n",
    "Uses linear interpolation between points\n",
    "\"\"\"\n",
    "function bellman_operator(wf::WaldFriedman, J)\n",
    "    c, L0, L1, f0, f1 = wf.c, wf.L0, wf.L1, wf.f0, wf.f1\n",
    "    m, pgrid = wf.m, wf.pgrid\n",
    "\n",
    "    J_out = zeros(m)\n",
    "    J_interp = LinInterp(pgrid, J)\n",
    "    \n",
    "    for (p_ind, p) in enumerate(pgrid)\n",
    "        # Payoff of choosing model 0\n",
    "        p_c_0 = payoff_choose_f0(wf, p)\n",
    "        p_c_1 = payoff_choose_f1(wf, p)\n",
    "        p_con = payoff_continue(wf, p, J_interp)\n",
    "\n",
    "        J_out[p_ind] = min(p_c_0, p_c_1, p_con)\n",
    "    end\n",
    "\n",
    "    return J_out\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This function takes a value function and returns the corresponding\n",
    "cutoffs of where you transition between continue and choosing a\n",
    "specific model\n",
    "\"\"\"\n",
    "function find_cutoff_rule(wf::WaldFriedman, J)\n",
    "    m, pgrid = wf.m, wf.pgrid\n",
    "\n",
    "    # Evaluate cost at all points on grid for choosing a model\n",
    "    p_c_0 = payoff_choose_f0(wf, pgrid)\n",
    "    p_c_1 = payoff_choose_f1(wf, pgrid)\n",
    "\n",
    "    # The cutoff points can be found by differencing these costs with\n",
    "    # the Bellman equation (J is always less than or equal to p_c_i)\n",
    "    lb = pgrid[searchsortedlast(p_c_1 - J, 1e-10)]\n",
    "    ub = pgrid[searchsortedlast(J - p_c_0, -1e-10)]\n",
    "\n",
    "    return lb, ub\n",
    "end\n",
    "\n",
    "function solve_model(wf; tol=1e-7)\n",
    "    bell_op(x) = bellman_operator(wf, x)\n",
    "    J =  compute_fixed_point(bell_op, zeros(wf.m), err_tol=tol, print_skip=5)\n",
    "\n",
    "    wf.sol.J = J\n",
    "    wf.sol.lb, wf.sol.ub = find_cutoff_rule(wf, J)\n",
    "    return J\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "This function takes an initial condition and simulates until it\n",
    "stops (when a decision is made).\n",
    "\"\"\"\n",
    "function simulate(wf::WaldFriedman, f; p0=0.5)\n",
    "    # Check whether vf is computed\n",
    "    if sumabs(wf.sol.J) < 1e-8\n",
    "        solve_model(wf)\n",
    "    end\n",
    "        \n",
    "    # Unpack useful info\n",
    "    lb, ub = wf.sol.lb, wf.sol.ub\n",
    "    drv = DiscreteRV(f)\n",
    "\n",
    "    # Initialize a couple useful variables\n",
    "    decision_made = false\n",
    "    decision = 0\n",
    "    p = p0\n",
    "    t = 0\n",
    "\n",
    "    while !decision_made\n",
    "        # Maybe should specify which distribution is correct one so that\n",
    "        # the draws come from the \"right\" distribution\n",
    "        k = draw(drv)[1]\n",
    "        t = t+1\n",
    "        p = bayes_update_k(wf, p, k)\n",
    "        if p < lb\n",
    "            decision_made = true\n",
    "            decision = 1\n",
    "        elseif p > ub\n",
    "            decision_made = true\n",
    "            decision = 0\n",
    "        end\n",
    "    end\n",
    "            \n",
    "    return decision, p, t\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "Uses the distribution f0 as the true data generating\n",
    "process\n",
    "\"\"\"\n",
    "function simulate_tdgp_f0(wf::WaldFriedman; p0=0.5)\n",
    "    decision, p, t = simulate(wf, wf.f0; p0=p0)\n",
    "\n",
    "    if decision == 0\n",
    "        correct = true\n",
    "    else\n",
    "        correct = false\n",
    "    end\n",
    "        \n",
    "    return correct, p, t\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "Uses the distribution f1 as the true data generating\n",
    "process\n",
    "\"\"\"\n",
    "function simulate_tdgp_f1(wf::WaldFriedman; p0=0.5)\n",
    "    decision, p, t = simulate(wf, wf.f1; p0=p0)\n",
    "\n",
    "    if decision == 1\n",
    "        correct = true\n",
    "    else\n",
    "        correct = false\n",
    "    end\n",
    "        \n",
    "    return correct, p, t\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "Simulates repeatedly to get distributions of time needed to make a\n",
    "decision and how often they are correct.\n",
    "\"\"\"\n",
    "function stopping_dist(wf::WaldFriedman; ndraws=250, tdgp=\"f0\")\n",
    "    if tdgp==\"f0\"\n",
    "        simfunc = simulate_tdgp_f0\n",
    "    else\n",
    "        simfunc = simulate_tdgp_f1\n",
    "    end\n",
    "        \n",
    "    # Allocate space\n",
    "    tdist = Array(Int64, ndraws)\n",
    "    cdist = Array(Bool, ndraws)\n",
    "\n",
    "    for i in 1:ndraws\n",
    "        correct, p, t = simfunc(wf)\n",
    "        tdist[i] = t\n",
    "        cdist[i] = correct\n",
    "    end\n",
    "        \n",
    "    return cdist, tdist\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use our types to solve the Bellman equation (*) and check whether it gives the same answer attained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute iterate 5 with error 0.0855260926408965\n",
      "Compute iterate 10 with error 0.0003878288254588469\n",
      "Compute iterate 15 with error 1.6097831208039537e-6\n",
      "Converged in 16 steps\n",
      "If this is true then both approaches gave same answer:\n",
      "true"
     ]
    }
   ],
   "source": [
    "wf = WaldFriedman(0.5, 5.0, 5.0, f0, f1; m=251)\n",
    "J2 = compute_fixed_point(x -> bellman_operator(wf, x), zeros(wf.m), err_tol=1e-6, print_skip=5)\n",
    "\n",
    "@printf(\"If this is true then both approaches gave same answer:\\n\")\n",
    "print(isapprox(J1, J2; atol=1e-5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical Example\n",
    "\n",
    "Now let's specify the two probability distibutions (the ones that we plotted earlier)\n",
    "\n",
    "* for $f_0$ we'll assume a beta distribution with parameters $a=1, b=1$\n",
    "\n",
    "* for $f_1$ we'll assume a beta distribution with parameters $a=9, b=9$\n",
    "\n",
    "The density of a  beta probability distribution with parameters $a$ and $b$ is\n",
    "\n",
    "$$ f(z; a, b) = \\frac{\\Gamma(a+b) z^{a-1} (1-z)^{b-1}}{\\Gamma(a) \\Gamma(b)}$$\n",
    "\n",
    "where $\\Gamma$ is the gamma function \n",
    "\n",
    "$$\\Gamma(t) = \\int_{0}^{\\infty} x^{t-1} e^{-x} dx$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Choose parameters\n",
    "c = 1.25\n",
    "L0 = 27.0\n",
    "L1 = 27.0\n",
    "\n",
    "# Choose n points and distributions\n",
    "m = 251\n",
    "f0 = pdf(Beta(2.5, 3), linspace(0, 1, m))\n",
    "f0 = f0 / sum(f0)\n",
    "f1 = pdf(Beta(3, 2.5), linspace(0, 1, m))\n",
    "f1 = f1 / sum(f1)  # Make sure sums to 1\n",
    "\n",
    "# Create an instance of our WaldFriedman class\n",
    "wf = WaldFriedman(c, L0, L1, f0, f1; m=m);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute iterate 5 with error 1.2384971736003685\n",
      "Compute iterate 10 with error 0.6235689198598084\n",
      "Compute iterate 15 with error 0.03178165128978527\n",
      "Compute iterate 20 with error 0.0005980373085616719\n",
      "Compute iterate 25 with error 1.1172556856564597e-5\n",
      "Compute iterate 30 with error 2.0872615458245036e-7\n",
      "Converged in 31 steps\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Bool[true,true,true,false,true,false,false,true,false,true  …  false,false,false,true,false,true,true,true,true,true],[1,1,2,1,1,3,3,1,1,1  …  1,1,1,1,1,1,1,1,1,1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve and simulate the solution\n",
    "cdist, tdist = stopping_dist(wf; ndraws=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value function equals $ p L_1$ for $p \\leq \\alpha$, and $(1-p )L_0$ for $ p \\geq \\beta$.\n",
    "Thus, the slopes of the two linear pieces of the value function are determined by $L_1$ and \n",
    "$- L_0$.  \n",
    "\n",
    "The value function is smooth in the interior region in which the probability assigned to distribution  $f_0$ is in the indecisive region $p \\in (\\alpha, \\beta)$.\n",
    "\n",
    "The decision maker continues to sample until the probability that he attaches to model $f_0$ falls below $\\alpha$ or above $\\beta$.\n",
    "\n",
    "The value function is smooth in the interior region in which the probability assigned to distribution  $f_0$ is in the indecisive region $p \\in (\\alpha, \\beta)$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to have some fun, you can change the cost parameters $L_0, L_1, c$, the parameters of two beta distributions $f_0$ and $f_1$, and the number of points and linear functions $m$ to use in our piece-wise continuous approximation to the value function. You can see the effects on the smoothness of the value function in the  middle range as you increase the numbers of functions in the piecewise linear approximation.  \n",
    "\n",
    "The function `stopping_dist` draws a number of simulations from $f_0$, computes a distribution of waiting times to making a decision, and displays a histogram of correct and incorrect decisions. (Here the correct decision occurs when $p_k$ eventually exceeds $\\beta$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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/>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = plot([f0 f1], \n",
    "    xlabel=L\"$k$ Values\",\n",
    "    ylabel=L\"Probability of $z_k$\",\n",
    "    labels=reshape([L\"$f_0$\"; L\"$f_1$\"], 1, 2),\n",
    "    linewidth=2,\n",
    "    title=\"Distributions over Outcomes\")\n",
    "\n",
    "b = plot(wf.pgrid, wf.sol.J, \n",
    "    xlabel=L\"$p_k$\",\n",
    "    ylabel=\"Value of Bellman\",\n",
    "    linewidth=2,\n",
    "    title=\"Bellman Equation\")\n",
    "    plot!(fill(wf.sol.lb, 2), [minimum(wf.sol.J); maximum(wf.sol.J)],\n",
    "    linewidth=2, color=:black, linestyle=:dash, label=\"\", ann=(wf.sol.lb-0.05, 5., L\"\\beta\"))\n",
    "    plot!(fill(wf.sol.ub, 2), [minimum(wf.sol.J); maximum(wf.sol.J)],\n",
    "    linewidth=2, color=:black, linestyle=:dash, label=\"\", ann=(wf.sol.ub+0.02, 5., L\"\\alpha\"),\n",
    "    legend=:none)\n",
    "\n",
    "counts = Array(Int64, maximum(tdist))\n",
    "for i in 1:maximum(tdist)\n",
    "    counts[i] = sum(tdist .== i)\n",
    "end\n",
    "c = bar(counts,\n",
    "    xticks=0:1:maximum(tdist),\n",
    "    xlabel=\"Time\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Stopping Times\",\n",
    "    legend=:none)\n",
    "\n",
    "counts = Array(Int64, 2)\n",
    "for i in 1:2\n",
    "    counts[i] = sum(cdist .== i-1)\n",
    "end\n",
    "d = bar([0; 1],\n",
    "    counts, \n",
    "    xticks=[0; 1],\n",
    "    title=\"Correct Decisions\", \n",
    "    ann=(-.4, 0.6 * sum(cdist), \"Percent Correct = $(sum(cdist)/length(cdist))\"),\n",
    "    legend=:none)\n",
    "\n",
    "plot(a, b, c, d, layout=(2, 2), size=(1200, 800))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Comparison with Neyman-Pearson formulation\n",
    "\n",
    "For several reasons, it is useful to describe the theory underlying test that Navy Captain G. S. Schuyler had been told to use and that led him to approach Milton Friedman and Allan Wallis to convey his conjecture that superior practical procedures existed.  Evidently,\n",
    "the Navy had told Captail Schuyler to use what it knew to be  a state-of-the-art Neyman-Pearson test. \n",
    "\n",
    "We'll rely on Abraham Wald's elegant summary of Neyman-Pearson theory.  For our purposes, watch for  there features of the setup:\n",
    "\n",
    "   * the assumption of a *fixed *sample size $n$\n",
    "   \n",
    "   * the application of  laws of large numbers, conditioned on alternative probability models, to interpret the probabilities $\\alpha$ and $\\beta$ defined in the Neyman-Pearson theory.\n",
    "   \n",
    "Recall that in the sequential analytic formulation above, that\n",
    "\n",
    "  * The sample size $n$ is not fixed but rather an object to be chosen; technically $n$ is a random variable. \n",
    "  \n",
    "  * The parameters $\\alpha$ and $\\beta$ characterize cut-off rules used to determine $n$ as a random variable.\n",
    "  \n",
    "  * Laws of large numbers make no appearances in the sequential construction.\n",
    "\n",
    "In chapter 1 of **Sequential Analysis** Abraham Wald summarizes the Neyman-Pearson approach to hypothesis testing.\n",
    "\n",
    "Wald frames the problem as making a decision about a probability distribution that is partially known. (You have to assume that *something* is already known in order to state a well posed problem.  Usually, *something* means *a lot*.)\n",
    "\n",
    "By restricting what is unknown, Wald uses the following simple structure to illustrate the main ideas.\n",
    "\n",
    "   * a decision maker wants to decide which of two distributions $f_0$,  $f_1$ govern an i.i.d. random variable $z$\n",
    "   \n",
    "   * The null hypothesis $H_0$ is the statement that $f_0$ governs the data.\n",
    "   \n",
    "   * The alternative hypothesis $H_1$ is the statement that $f_1$ governs the data. \n",
    "   \n",
    "   * The problem is to devise and analyze a test of hypthothesis $H_0$ against the alternative hypothesis $H_1$ on the basis of a sample of a fixed number $n$ independent observations $z_1, z_2, \\ldots, z_n$ of the random variable $z$.  \n",
    "   \n",
    "To quote Abraham Wald,\n",
    "\n",
    "  * *A test procedure leading to the acceptance or rejection of the hypothesis in question is simply a rule specifying, for each possible sample of size $n$, whether the hypothesis should be accepted or rejected on the basis of the sample. This may also be expressed as follows: A test procedure is simply a subdivision of the totality of all possibsle samples of size $n$ into two mutually exclusive parts, say part 1 and part 2, together with the application of the rule that the hypothesis be accepted if the observed sample is contained in part 2.  Part 1 is also called the critical region.  Since part 2 is the totality of all samples of size 2 which are not included in part 1, part 2 is uniquely determined by part 1.  Thus, choosing a test procedure is equivalent to determining a critical region.* \n",
    "  \n",
    "Let's listen to Wald longer:\n",
    "\n",
    "  * *As a basis for choosing among critical regions the following considerations have been advanced by Neyman and Pearson: In accepting or rejecting $H_0$ we may commit errors of two kinds. We commit an error of the first kind if we reject $H_0$ when it is true; we commit an error of the second kind if we accept $H_0$ whe $H_1$ is true.  After a particular critical region $W$ has been chosen, the probability of committing an error of the first kind, as well as the probability of committing an error of the second kind is uniquely determined.  The probability of committing an error of the first kind is equal to the probability, determined by the assumption that $H_0$ is true, that the observed sample will be included in the critical region $W$.  The probability of committing an error of the second kind is equal to the probability, determined on the assumption that $H_1$ is true, that the probability will fall outside the critical region $W$.  For any given critical region $W$ we shall denote the probability of an error of the first kind by $\\alpha$ and the probability of an error of the second kind by $\\beta$.*\n",
    "\n",
    "\n",
    "Let's listen carefully to how Wald applies a law of large numbers to interpret $\\alpha$ and $\\beta$:\n",
    "\n",
    "  * *The probabilities $\\alpha$ and $\\beta$ have the following important practical interpretation: Suppose that we draw a large number of samples of size $n$.  Let $M$ be the number of such samples drawn.  Suppose that for each of these $M$ samples we reject $H_0$ if the sample is included in $W$ and accept $H_0$ if the sample lies outside $W$.  In this way we make $M$ statements of rejection or acceptance.  Some of these statements will in general be wrong.  If $H_0$ is true and if $M$ is large, the probability is nearly $1$ (i.e., it is practically certain) that the proportion of wrong statements (i.e., the number of wrong statements divided by $M$) will be approximately $\\alpha$.  If $H_1$ is true, the probability is nearly $1$ that the proportion of wrong statements will be approximately $\\beta$.  Thus, we can say that in the long run [ here Wald applies a law of large numbers by driving $M \\rightarrow \\infty$ (our comment, not Wald's) ] the proportion of wrong statements will be $\\alpha$ if $H_0$is true and $\\beta $ if $H_1$ is true.*\n",
    "\n",
    "The quantity $\\alpha$ is called the *size* of the critical region, and the quantity $1-\\beta$ is called the *power* of the critical region.\n",
    "\n",
    "Wald notes that \n",
    "\n",
    "  * *one critical region $W$ is more desirable than another if it has smaller values of $\\alpha$ and $\\beta$.  Although either $\\alpha$ or $\\beta$ can be made arbitrarily small by a proper choice of the critical region $W$, it is possible to make both $\\alpha$ and $\\beta$ arbitrarily small for a fixed value of $n$, i.e., a fixed sample size.*\n",
    "\n",
    "\n",
    "Wald summarizes Neyman and Pearson's setup as follows:\n",
    "\n",
    " * *Neyman and Pearson show that a region consisting of all samples $(z_1, z_2, \\ldots, z_n)$ which satisfy the inequality \n",
    " $$\\frac{ f_1(z_1) \\cdots f_1(z_n)}{f_0(z_1) \\cdots f_1(z_n)} \\geq k $$ is a most powerful critical region for testing the hypothesis $H_0$ against the alternative hypothesis $H_1$. The term $k$ on the right side is a constant chosen so that the region will have the required size $\\alpha$.*\n",
    " \n",
    "Wald goes on to discuss Neyman and Pearson's concept of *uniformly most powerful* test. \n",
    "\n",
    "\n",
    "Here is how Wald introduces the notion of a sequential test\n",
    "•A rule is given for making one of the following three decisions at any stage of the experiment (at the m th trial for each integral value of m ): (1) to accept the hypothesis H , (2) to reject the hypothesis H , (3) to continue the experiment by making an additional observation. Thus, such a test procedure is carried out sequentially. On the basis of the first observation one of the aforementioned decisions is made. If the first or second decision is made, the process is terminated. If the third decision is made, a second trial is performed. Again, on the basis of the first two observations one of the three decisions is made. If the third decision is made, a third trial is performed, and so on. The process is continued until either the first or the second decisions is made. The number n  of observations required by such a test procedure is a random variable, since the value of n  depends on the outcome of the observations.\n"
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